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Teleoperated mobile robots enable humans to explore, inspect, and monitor hazardous and remote environments.
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Map building of indoor environments is considered a basic building block for autonomous mobile robots, enabling, among others, self-localization and efficient path planning.
The ability to reliably detect vegetation is an important requirement for outdoor navigation with mobile robots as it enables the robot to navigate more efficiently and safely.
Time to contact or time to collision (TTC) is utmost important information for animals as well as for mobile robots because it enables them to avoid obstacles; it is a convenient way to analyze the surrounding environment.
Visual landmarks selected via the proposed method enable mobile robots to identify location using densely packed knowledge.
The intelligent dynamic selection and synchronization of selected behaviors enable mobile robots to perform many tasks under complex situations.
High-level control systems are designed to enable mobile robots to successfully perform complex missions such as office delivery and survillance tasks.
The goal is to determine the suitability of the state-of-the-art vision algorithms for mobile robot domains, and to identify the challenges that still need to be addressed to enable mobile robots to learn and adapt models for color, so as to operate autonomously in natural conditions.
In addition to other high-level computing applications, the mobile robots run a condensed version of MATLAB, enabling ANN controllers evolved in simulation to be transferred directly onto physical robots without any alteration to the code.
Widespread use of mobile robots can only be achieved when framworks that enable specification, design and implementation of systems are available.
In addition to autonomous abilities, mobile robots need to facilitate the human robot interaction control mode that enables human users to easily control or collaborate with robots.
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